For mapping applications, maps and road traffic patterns have been built using only road geometry and intersections with little or no lane-level insight. As a result, it is difficult to report upcoming traffic congestion. The traffic attempting to turn off at the next junction might be backed up, while the cars that are going past it on the inner lane are travelling at normal speeds or vice versa. The difference in lane traffic and speeds may cause inefficiencies in routing and usage of a roadway network. The inefficiencies that come from poor choice of routes and lanes by individual drivers lead to congestion and delay for both the individual driver and other individuals traveling in other vehicles. Vehicles are unable to get to a destination on time and often are not certain of the best routes to take or which lanes of travel would be the fastest for their mission.
Existing navigation systems provide route guidance primarily by choosing routes based upon expected road segment travel times, turn penalties, and preferences for use of certain types or classes of roads. These systems may readily identify the locations where mandatory lane changes are required to traverse a recommended or chosen path but are not sensitive to the best lanes to use in various traffic situations and in different locations.
With lane level data, more effective routes and lane choices may be determined. However, even after acquiring lane level traffic data, generating accurate lane level traffic flow predictions may be inaccurate. The problem is that even though some services can predict lane level traffic, the services lacks a very important information which is the dynamics of traffic flow around lane transitions and lane maneuvers. While identifying that one lane is moving faster than another may be beneficial, without identifying how or when to switch into the lane, the lane level traffic information may lead to incomplete or erroneous results.